Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	123,934
2	South Dakota	115,954
3	Utah	93,922
4	Wisconsin	93,537
5	Iowa	93,424
6	Rhode Island	92,144
7	Tennessee	91,925
8	Nebraska	90,383
9	Kansas	84,239
10	Idaho	83,140
11	Arizona	82,726
12	Indiana	82,530
13	Arkansas	82,463
14	Oklahoma	81,018
15	Wyoming	80,598
16	Illinois	80,494
17	Alabama	80,414
18	Montana	80,155
19	Nevada	79,254
20	Mississippi	78,512
21	Minnesota	76,602
22	Louisiana	73,445
23	New Mexico	73,184
24	Missouri	72,556
25	Alaska	67,481
26	Florida	67,476
27	Georgia	67,133
28	Kentucky	67,031
29	South Carolina	66,846
30	California	66,810
31	Texas	66,771
32	Ohio	65,240
33	Delaware	65,213
34	New Jersey	64,372
35	Colorado	62,073
36	Massachusetts	60,895
37	Connecticut	57,777
38	North Carolina	57,702
39	New York	56,632
40	Michigan	55,840
41	Pennsylvania	55,400
42	West Virginia	54,626
43	Maryland	49,585
44	Virginia	45,447
45	District of Columbia	44,076
46	New Hampshire	36,884
47	Puerto Rico	36,222
48	Washington	35,869
49	Oregon	29,126
50	Maine	21,132
51	Hawaii	16,169
52	Vermont	13,812

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	1,395
2	Utah	1,264
3	Tennessee	1,238
4	Kansas	1,225
5	Rhode Island	1,183
6	Kentucky	1,114
7	Arkansas	1,101
8	Massachusetts	1,089
9	California	1,079
10	Louisiana	1,059
11	South Carolina	1,042
12	Oklahoma	1,037
13	Alabama	998
14	Indiana	966
15	Georgia	942
16	Mississippi	920
17	New York	910
18	Nevada	908
19	Florida	886
20	West Virginia	880
21	North Carolina	854
22	Connecticut	843
23	Texas	826
24	Delaware	822
25	New Mexico	788
26	Ohio	787
27	Pennsylvania	767
28	New Jersey	763
29	Wisconsin	728
30	Iowa	682
31	Missouri	677
32	Illinois	670
33	Montana	667
34	Idaho	642
35	Virginia	624
36	Wyoming	620
37	Nebraska	610
38	South Dakota	566
39	Maryland	543
40	New Hampshire	529
41	Colorado	519
42	Alaska	484
43	Maine	456
44	District of Columbia	444
45	Michigan	427
46	North Dakota	413
47	Washington	412
48	Minnesota	395
49	Vermont	310
50	Puerto Rico	286
51	Oregon	263
52	Hawaii	163

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,224
2	New York	1,996
3	Massachusetts	1,883
4	Rhode Island	1,808
5	North Dakota	1,784
6	Connecticut	1,773
7	South Dakota	1,758
8	Mississippi	1,713
9	Louisiana	1,684
10	Illinois	1,508
11	Michigan	1,392
12	Arizona	1,369
13	Pennsylvania	1,361
14	Indiana	1,320
15	Arkansas	1,314
16	Iowa	1,307
17	New Mexico	1,293
18	District of Columbia	1,146
19	Nevada	1,106
20	South Carolina	1,106
21	Tennessee	1,105
22	Kansas	1,080
23	Alabama	1,058
24	Florida	1,055
25	Texas	1,038
26	Georgia	1,032
27	Maryland	1,028
28	Missouri	1,022
29	Minnesota	1,008
30	Delaware	982
31	Montana	981
32	Wisconsin	949
33	Nebraska	928
34	Colorado	904
35	West Virginia	867
36	Idaho	853
37	Wyoming	844
38	Ohio	816
39	California	739
40	Kentucky	714
41	North Carolina	703
42	Oklahoma	683
43	New Hampshire	622
44	Virginia	622
45	Puerto Rico	498
46	Washington	494
47	Utah	431
48	Oregon	376
49	Maine	316
50	Alaska	292
51	Vermont	250
52	Hawaii	212

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	28
2	Kansas	28
3	Pennsylvania	21
4	Alabama	20
5	West Virginia	20
6	New Mexico	18
7	Nevada	17
8	Tennessee	17
9	South Dakota	16
10	Arkansas	14
11	California	14
12	Illinois	14
13	Louisiana	14
14	Maine	14
15	Mississippi	14
16	New Jersey	14
17	Rhode Island	14
18	Wyoming	14
19	Iowa	13
20	Montana	13
21	New Hampshire	13
22	Texas	13
23	Connecticut	12
24	Massachusetts	12
25	South Carolina	12
26	Indiana	11
27	Oklahoma	11
28	Michigan	10
29	Nebraska	10
30	North Dakota	10
31	Idaho	9
32	Minnesota	9
33	New York	9
34	North Carolina	9
35	Wisconsin	9
36	Colorado	8
37	Missouri	8
38	Ohio	8
39	Florida	7
40	Georgia	7
41	Kentucky	7
42	Maryland	7
43	Utah	7
44	Washington	6
45	Virginia	4
46	Delaware	3
47	District of Columbia	3
48	Hawaii	3
49	Oregon	3
50	Puerto Rico	3
51	Vermont	3
52	Alaska	2

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	277,677	1	99
Dewey	South Dakota	225,390	2	99
Lincoln	Arkansas	222,052	3	99
Norton	Kansas	221,787	4	99
Bent	Colorado	219,114	5	99
Davidson	Tennessee	107,367	276	91
Richland	South Carolina	69,167	1601	49
York	South Carolina	61,588	1976	37
Orange	California	60,398	2028	35
Pierce	Washington	33,128	2848	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Iron	Wisconsin	6,154	5	99
Davidson	Tennessee	885	1837	41
Richland	South Carolina	844	1896	39
York	South Carolina	673	2193	30
Orange	California	629	2252	28
Pierce	Washington	428	2589	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.